# 0-1 Knapsack Problem

Difficulty: Medium, Asked-In: Amazon, Microsoft, Yahoo, Zoho, Visa.

Key takeaway: An excellent problem to learn problem solving using dynamic programming.

### Let’s understand the problem

We are given n items, where each item has a weight and profit associated with it. We are also given a knapsack with capacity C (knapsack can hold at most C weight). Write a program to determine the maximum profit that can be obtained by selecting a subset of items without exceeding the knapsack capacity.

• P[]: Array containing the profits of items.
• W[]: Array containing the weights of items.
• n: Number of items in the input.
• C: Capacity of the knapsack.

Constraint

• We can either put an item completely into the knapsack or not put it at all. In other words, it is not possible to put a part of an item into the knapsack. That's why the name of the problem is 0-1 knapsack problem.
• No item can be selected more than once.

#### Examples

Input: n = 3, W[] = [3, 1, 4], P[] = [25, 20, 20], C = 5

Output: 45

Explanation: There are 7 possible non-empty subsets of items: {3}, {1}, {4}, {3}, {3, 1}, {3, 4}, {1, 4}, and {3, 1, 4}. Out of these choices, {3, 4} and {3, 1, 4} have combined weights that exceed the maximum capacity of 5. So we will not consider them in the solution. Among the remaining subsets, {3}, {1}, {4}, {3}, {3, 1}, and {1, 4}, the subset {3, 1} will give the maximum profit, which is 45.

Input: n = 3, W[] = [4, 5, 1], P[] = [1, 2, 3], C = 4

Output: 3

Explanation: There are two items which have a weight less than or equal to 4. If we select the item with weight 4, the profit is 1. And if we select the item with weight 1, the profit is 3. So the maximum possible profit is 3. Note: we cannot put items with weights 4 and 1 together because the capacity of the knapsack is 4.

Input: n = 3, W[] = [4, 5, 6], P[] = [1, 2, 3], C = 3

Output: 0

Explanation: The weight of each item exceeds the maximum capacity of the knapsack, so we cannot select any items. The maximum possible profit is 0.

### Discussed solution approaches

• Brute force approach using recursion
• Efficient approach using top-down memoization
• Efficient approach using bottom-approach of DP
• Space-optimized version of the bottom-approach
• Another space-optimized version using a 1-D table

### Brute force approach using recursion

One basic solution will be to generate all possible subsets of items and calculate the combined weight of each subset. If the weight of the subset is greater than the knapsack capacity C, we ignore that subset. Otherwise, we calculate the profit associated with that subset and update the maximum profit. In other words: we only choose the subsets with a weight smaller than the knapsack capacity C. Among these subsets, we identify the subset with the maximum profit.

The critical question is: How can we generate all subsets of items? Here is an idea! There are two choices for every item: Either item is included in the knapsack or item is excluded from the knapsack. Let's move forward to discuss these two scenarios separately. Suppose we use the function knapsack(P[], W[], n, C) to find the maximum profit for n items.

#### Case 1: If we include nth items in the knapsack

If the weight of the nth item (W[n - 1]) is less than or equal to the knapsack capacity C, we can include nth item in the knapsack. If we include the nth item, we will acquire P[n - 1] profit, and the remaining knapsack capacity will be C - W[n - 1].

Now problem gets reduced to a smaller version of the knapsack problem: Maximizing the profit from the remaining n - 1 items with capacity C - W[n - 1]. So, we call the same function recursively with new input parameters, i.e., knapsack(P[], W[], n - 1, C - W[n - 1]).

``````If (C >= W[n-1])
profitInclude = P[n-1] + knapsack(P[], W[], n-1, C - W[n-1])``````

#### Case 2: If we exclude the nth item from the knapsack

If we exclude the nth item, the capacity of the knapsack remains the same, but we have only n - 1 items left. Now, the problem is reduced to another smaller version of the knapsack problem: Maximizing the profit from the remaining n - 1 items with a capacity of C. So, we call the same function recursively with updated input parameters, i.e., knapsack(P[], W[], n - 1, C).

``profitExclude = knapsack(P[], W[], n-1, C)``

The maximum profit obtained from n items will be the max of the above two cases i.e., max (profitInclude, profitExclude).

Base case: If there are no more items to consider (n == 0) or the knapsack capacity becomes zero (C == 0), we can’t select any more items. So, we return 0 as the maximum profit.

#### Solution code C++

``````int knapsack(int P[], int W[], int n, int C)
{
if (n == 0 || C == 0)
return 0;

int profitInclude = 0;
if (C >= W[n - 1])
profitInclude = P[n - 1] + knapsack(P, W, n - 1, C - W[n - 1]);

int profitExclude = knapsack(P, W, n - 1, C);

return max(profitInclude, profitExclude);
}   ``````

#### Solution code Python

``````def knapsack(P, W, n, C):
if n == 0 or C == 0:
return 0

profitInclude = 0
if C >= W[n - 1]:
profitInclude = P[n - 1] + knapsack(P, W, n - 1, C - W[n - 1])

profitExclude = knapsack(P, W, n - 1, C)

return max(profitInclude, profitExclude)``````

#### Time complexity analysis

There are two possibilities for each of the n items. So the total number of possible subsets = (2 * 2 * 2 * ...n times) = 2^n. In the worst case, recursion will explore all these possible combinations of items. So time complexity = O(2^n).

This solution is inefficient because recursion will solve the same sub-problems repeatedly. We can observe repeated sub-problems by drawing the recursion tree. In the following image, one sub-problem is repeated. If we expand the recursion tree further, we will observe more repeated sub-problems.

### Efficient approach using top-down memoization

#### Solution idea

Now, the critical question is: How can we improve the time complexity? This is an optimization problem where sub-problems are repeated during recursion. So we can think to apply the dynamic programming approach and store the sub-problems solution in extra memory to avoid repeated calculation.

One idea is to use the top-down approach of dynamic programming (memoized version of the above recursive solution). Here is the idea: When any subproblem appears first time during recursion, we calculate it and store that value in an extra memory or look-up table. When the same subproblem appears again, we directly retrieve the stored solution from the extra memory or look-up table in constant time.

#### Solution steps

We initialize a 2D table dp[][] of size (n + 1) * (C + 1) and set all values to -1. We use this table to store the solutions of the sub-problems. The critical question is: Why (n + 1) * (C + 1)? During recursion, input size n and C decrease by at least 1 until they reach 0. So there can be (n + 1) * (C + 1) possible sub-problems, and we need to allocate an entry for each one of them.

Now, we call a helper function knapsack(P[], W[], i, C, dp), where the initial value of i is n.

1. Base case: If i == 0 or C == 0, we return 0 as the maximum profit. If the base case is false, we check if the solution for the current state (dp[i][C]) is already computed or not.
2. If dp[i][C] < 0, result is not yet computed. So, we proceed with the recursive calculation.

• We calculate the profit by including the ith item if C >= W[i-1] i.e. profitInclude = P[i - 1] + knapsack(P, W, i - 1, C - W[i - 1], dp).
• We calculate the profit by excluding the ith item i.e. profitExclude = knapsack(P, W, i - 1, C, dp).
• We store the maximum profit i.e. dp[i][C] = max (profitInclude, profitExclude).
3. If dp[i][C] > 0, it means the result has already been calculated and stored in dp[i][C]. So there is no need to do the calculation again, so we will directly return the value stored at dp[i][C].

#### Solution code C++

``````int knapsack(int P[], int W[], int i, int C, int** dp)
{
if (i == 0 || C == 0)
return 0;

if (dp[i][C] < 0)
{
int profitInclude = 0;
if (C >= W[i-1])
profitInclude = P[i-1] + knapsack(P, W, i - 1, C - W[i-1], dp);

int profitExclude = knapsack(P, W, i - 1, C, dp);
dp[i][C] = max(profitInclude, profitExclude);
}
return dp[i][C];
}

int zeroOneKnapSack(int P[], int W[], int n, int C)
{
int** dp = new int*[n + 1];
for (int i = 0; i <= n; i = i + 1)
{
dp[i] = new int[C + 1];
for (int j = 0; j <= C; j = j + 1)
dp[i][j] = -1;
}
int result = knapsack(P, W, n, C, dp);
return result;
}``````

#### Solution code Python

``````def knapsack(P, W, i, C, dp):
if i == 0 or C == 0:
return 0

if dp[i][C] < 0:
profitInclude = 0
if C >= W[i - 1]:
profitInclude = P[i - 1] + knapsack(P, W, i - 1, C - W[i - 1], dp)

profitExclude = knapsack(P, W, i - 1, C, dp)
dp[i][C] = max(profitInclude, profitExclude)

return dp[i][C]

def zeroOneKnapSack(P, W, n, C):
dp = [[-1] * (C + 1) for _ in range(n + 1)]

result = knapsack(P, W, n, C, dp)
return result``````

#### Time and space complexity analysis

We solve each sub-problem once using recursion. So time complexity = O(n*C). This is a significant improvement compared to the previous approach!

We require extra memory of size (n + 1)(C + 1). So space complexity =  O(nC). There will be additional overhead of space complexity for the recursion call stack. So, What would be the size of the recursion call stack? Explore and think!

### Efficient approach using bottom-approach of DP

#### Solution idea

This is a dynamic programming problem. So, we can efficiently solve this problem using the iterative idea of bottom-up approach. How can we do this? Let's think!

We start by solving subproblems with smaller sizes (0 items and 0 capacity). Then, we build the solution iteratively for larger subproblems using the previously solved smaller subproblems until we find the solution to the given problem (n items and C capacity). During this process, we use additional memory to store the solution of each subproblem.

#### Solution steps

Table size: We define a 2D table dp[n + 1][C + 1] to store the solution for each possible subproblem.

Table initialization: Now, we initialize the table with the solution of the smallest version of the subproblem (similar to the base case in the recursive solution). For this, we set the value 0 in the first row and the first column of the table.

Iterative structure to fill the table: Now, we define an iterative structure to fill the table and store the solution of subproblems in a bottom-up manner. We can get this insight from the recursive structure of the above recursive solution. Here are the steps!

We use nested loops to fill the remaining cells of the dp table. The outer loop runs from i = 1 to n to iterate over each item, and the inner loop runs from j = 1 to C to consider each possible capacity value. At each ith iteration:

• If weight of the ith item (W[i - 1]) is greater than the current knapsack capacity j, the ith item cannot be included in the knapsack. So at dp[i][j], we copy the value stored at dp[i - 1][j]. Note: dp[i - 1][j] stores the maximum profit for knapsack capacity j after considering the i - 1 items.
• Otherwise, we consider two possibilities: 1) Solution for not including the ith item, i.e., dp[i - 1][j], 2) Solution for including the ith item, i.e., P[i - 1] + dp[i - 1][j - W[i - 1]]. We take the maximum of both possibilities and assign it to dp[i][j]. Note: dp[i - 1][j - W[i - 1]] stores the maximum profit for knapsack capacity j - W[i - 1] after considering the i - 1 items.
``````if (W[i - 1] > j)
dp[i][j] = dp[i - 1][j];
else
dp[i][j] = max(P[i - 1] + dp[i - 1][j - W[i - 1]], dp[i - 1][j]);``````

Returning the final solution: After filling the entire dp table, we return the value stored in the bottom-right cell dp[n][C], which represents the maximum profit after considering all n items and knapsack capacity C.

#### Solution code C++

``````int knapsack(int P[], int W[], int n, int C)
{
int dp[n + 1][C + 1];

for (int i = 0; i <= n; i++)
dp[i][0] = 0;

for (int j = 0; j <= C; j++)
dp[0][j] = 0;

for (int i = 1; i <= n; i++)
{
for (int j = 1; j <= C; j++)
{
if (W[i - 1] > j)
dp[i][j] = dp[i - 1][j];
else
dp[i][j] = max(P[i - 1] + dp[i - 1][j - W[i - 1]], dp[i - 1][j]);
}
}

return dp[n][C];
}``````

#### Solution code Python

``````def knapsack(P, W, n, C):
dp = [[0] * (C + 1) for _ in range(n + 1)]

for i in range(1, n + 1):
for j in range(1, C + 1):
if W[i - 1] > j:
dp[i][j] = dp[i - 1][j]
else:
dp[i][j] = max(P[i - 1] + dp[i - 1][j - W[i - 1]], dp[i - 1][j])

return dp[n][C]``````

#### Time and space complexity analysis

We use two separate loops for initialization and one nested loop for storing results in the table. During each iteration of the loop, we perform constant operations. So time complexity = O(n) + O(C) + O(n*C) = O(n*C). We use an extra 2D table of size (n + 1)*(C + 1), so space complexity = O(n*C).

This approach is slightly more efficient compared to the top-down approach. The reason is simple: there is no overhead of recursive calls and call stack.

### Space-optimized version of the bottom-approach

#### Solution idea

The above solution uses O(n*C) space. The critical question is: Can we reduce the space further? Can we use a table with two rows to get the solution? Let's think!

In the above solution, we can observe a pattern: To fill the (i, j) entry, we use two values stored in the previous row at (i - 1, j) and (i - 1, j - W[i - 1]). In other words, for filling the ith row, we use the value stored in the (i - 1)th row. So, we only need two rows at a time: previous row and current row.

We define a 2D dp[][] array of size 2 *(C + 1) to represent the two rows: dp[0] and dp[1]. We store ith row values at index i % 2 and previous row values at index (i - 1) % 2. Taking modulus with 2 helps us switch between rows based on the current row index i.

For example, for i = 3, the previous row will be stored at dp[0] and the current row will be filled at dp[1]. Then, for i = 4, dp[1] will contain the entries of the previous row and the current row will be filled at dp[0] (overriding the previous value stored in dp[0]). This is the idea of a rolling array in dynamic programming.

So there are two major changes in the above solution: 1) Instead of an (n + 1) x (C + 1) table, we use a 2 x (C + 1) table. 2) Instead of using i and i - 1, we use i % 2 and (i - 1) % 2. The remaining ideas will remain the same!

#### Solution code C++

``````int knapsack(int P[], int W[], int n, int C)
{
int dp[2][C + 1];
for (int i = 0; i <= n; i++)
{
for (int j = 0; j <= C; j++)
{
if (i == 0 || j == 0)
dp[i % 2][j] = 0;
else if (W[i - 1] <= j)
dp[i % 2][j] = max(P[i - 1] + dp[(i - 1) % 2][j - W[i - 1]], dp[(i - 1) % 2][j]);
else
dp[i % 2][j] = dp[(i - 1) % 2][j];
}
}
return dp[n % 2][C];
}``````

#### Solution code Python

``````def knapsack(P, W, n, C):
dp = [[0] * (C + 1) for _ in range(2)]

for i in range(n + 1):
for j in range(C + 1):
if i == 0 or j == 0:
dp[i % 2][j] = 0
elif W[i - 1] <= j:
dp[i % 2][j] = max(P[i - 1] + dp[(i - 1) % 2][j - W[i - 1]], dp[(i - 1) % 2][j])
else:
dp[i % 2][j] = dp[(i - 1) % 2][j]

return dp[n % 2][C]``````

#### Time and space complexity analysis

The time complexity of this approach will be the same as the previous approach, i.e., O(n*C). However, the space complexity will be reduced to O(C) because we are using only a table with two rows.

### Another space-optimized version using a 1-D array

#### Solution idea

The above solution works well in O(C) space. The critical question is: Can we solve 0-1 knapsack problem using a single row or 1-D array? The idea is: If we start traversing rows from right to left, then it can be done with a single row only. How? Let's think!

Suppose we take a 1-D table dp[C + 1] and initialize all values with 0. Now at the (i - 1)th iteration, suppose we have stored (i - 1)th row in the table. To calculate the values at each cell dp[j] in the ith iteration (from right to left), we have two values available from (i - 1)th iteration: dp[j] and dp[j - W[i - 1]].

In other words, at the start of the ith iteration:

• dp[j] will store a value equivalent to dp[i - 1][j].
• dp[j - W[i - 1]] will store a value equivalent to dp[i - 1][j - W[i - 1]].

So, when we fill the value at dp[j] while traversing from right to left in the ith iteration (equivalent to dp[i][j]), we already have required values available at dp[j] and dp[j - W[i - 1]].

#### Solution code C++

``````int knapsack(int P[], int W[], int n, int C)
{
int dp[C + 1];
memset(dp, 0, sizeof(dp));

for (int i = 1; i < n + 1; i++)
{
for (int j = C; j >= 0; j--)
{
if (W[i - 1] <= j)
dp[j] = max(dp[j], dp[j - W[i - 1]] + P[i - 1]);
}
}

return dp[C];
}``````

#### Solution code Python

``````def knapsack(P, W, n, C):
dp = [0] * (C + 1)

for i in range(1, n + 1):
for j in range(C, -1, -1):
if W[i - 1] <= j:
dp[j] = max(dp[j], dp[j - W[i - 1]] + P[i - 1])

return dp[C]``````

#### Time and space complexity analysis

The time and space complexity of this approach will be the same as the previous approach, i.e., O(n*C) and O(C). However, the space complexity will be reduced because we are only using a 1-D table instead of a two-row 2D table.

### Critical ideas to think!

• What would be the size of the recursion call stack in the recursive solution?
• What would be the solution idea when we have an infinite supply of each item? In other words, how can we approach this problem when we have the option to select any item more than once?
• Fractional Knapsack Problem: Suppose items are like grains, where we have the option to select a fraction of items. How can we solve this problem?
• Which approach is more efficient in terms of time and space complexity: top-down approach or bottom-up approach?
• Can we think to implement the last approach by traversing from left to right?
• In the top-down approach, why are we initializing table values as -1?
• What would be the best and worst-case scenarios in the case of a recursive solution?
• In the space-optimized solution, instead of using i % 2, can we use flag^1? If the flag is 0, the result of the XOR operation will be 1, and if the flag is 1, the result will be 0. So it will flip the value of the flag variable between 0 and 1.

### Similar coding questions to practice

• Rod Cutting Problem
• Coin change problem
• Weighted Job Scheduling
• Subset Sum Problem
• Equal Sum Partition
• Count of Subset Sum
• Minimum Subset Sum Difference

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